> For the complete documentation index, see [llms.txt](https://doc.batch.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://doc.batch.com/guides-and-best-practices/batch-ai/how-to-time-your-reactivation-campaigns-around-each-customers-predicted-churn-date.md). # How to Time Your Reactivation Campaigns Around Each Customer's Predicted Churn Date ## What Is the Decisive Churn Date Score? Batch AI Predict's **Decisive Churn Date** score predicts, for each customer in your database, the date at which they are at high risk of becoming inactive. The output is a date attribute on the customer profile rather than a float; this makes it particularly well suited to date-based orchestration. The score is recalculated on a regular basis. As customer behavior evolves, predicted dates are updated accordingly. {% hint style="warning" %} **Decisive Churn Date vs. Subscription Churn: which one do you need?** * **Decisive Churn Date** → You want to know *when* a customer is at risk of going inactive, so you can time your intervention precisely. Best for non-subscription businesses. * [**Subscription Churn**](/guides-and-best-practices/batch-ai/how-to-identify-and-retain-customers-at-risk-of-cancelling-their-subscription.md) → You want to know the *probabili ty* that a subscriber will cancel before their contract ends. Best for subscription-based models. {% endhint %} ## Why Use It? Standard reactivation campaigns operate on fixed delays: "if no purchase in 90 days, send a win-back email." The problem is that not every customer follows the same rhythm. Sending too early wastes budget; sending too late misses the window. With the Decisive Churn Date score, you can: * **Trigger reactivation at the right moment for each customer**, rather than applying the same delay to everyone. * **Concentrate retention budget** on the customers who are actually approaching their churn point. * **Sequence your messages** with escalating intensity as the predicted date approaches. ## How to Use This Score in Batch ### 1. Orchestration: Trigger Retention Relative to the Predicted Date (Primary Use) The core use case. Set up a date-based automation that fires at a defined number of days before each customer's predicted churn date. **How to set it up:** 1. Go to **Automations** in your Batch dashboard. 2. Trigger an automation using the `Decisive churn date` attribute. 3. Define your message timing relative to that date: * First message: J-30 (early nudge, low intensity) * Second message: J-7 (stronger incentive or personalized offer) 4. Add an **exit condition**: if the customer makes a purchase before the sequence ends, remove them from the flow. 5. From J-7 onward, consider **excluding these customers from other campaigns** to avoid diluting the retention message. {% hint style="info" %} Use a [**Control Group**](/guides-and-best-practices/orchestration/how-to-use-the-random-split-feature.md) on your automation to measure the incremental impact of the date-timed sequence versus no intervention (or your existing fixed-delay reactivation). {% endhint %} ### 2. Segmentation: Target Customers Approaching Their Churn Date If you prefer a campaign-based approach, you can filter customers whose predicted churn date falls within a defined window. **How to set it up:** 1. Go to **Campaigns** in your Batch dashboard and create a new campaign. 2. In the targeting step, apply a date condition: `Decisive churn date is within the next 60 days` (or your preferred horizon). 3. Optionally combine with a recency or value filter to prioritize your highest-value at-risk customers. ### 3. Personalization: Adapt Offer Intensity to Proximity You can also use the predicted date to calibrate the content of your retention message: the closer the churn date, the stronger the incentive. **Example logic:** * Churn date in more than 60 days → soft reminder of value or loyalty benefits * Churn date in less than 30 days → personalized offer with a time-limited discount ## Real-World Use Cases | **Industry** | **Example use case** | | ------------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | **Retail** | Customers whose predicted churn date is approaching: trigger a "we miss you" email at J-30 and a personalized incentive at J-7, with exit condition on first purchase | | **Travel** | Travelers who have not booked in a long time: send a personalized destination suggestion timed around their predicted inactivity date | | **Beauty & Cosmetics** | Customers nearing their churn date: trigger a replenishment reminder or a "best picks for you" email before they go fully inactive | | **Media & Entertainment** | Users whose engagement is declining toward the predicted churn date: send a curated content highlight or an exclusive offer sequenced around that date | ## Related Documentation * [How to Measure the Performance of Batch AI Predict Scores](/guides-and-best-practices/batch-ai/how-to-measure-the-performance-of-batch-ai-predict-scores.md) * [Segments](https://doc.batch.com/getting-started/features/customer-engagement-platform/profiles/segments) * [Orchestration Targeting](https://doc.batch.com/getting-started/features/customer-engagement-platform/orchestration/targeting) * [Orchestration Analytics](https://doc.batch.com/getting-started/features/customer-engagement-platform/analytics/orchestration-analytics) * [Conversion Goal dedicated insights](https://doc.batch.com/getting-started/features/customer-engagement-platform/analytics/orchestration-analytics#conversion-goal-dedicated-insights) --- # Agent Instructions This documentation is published with GitBook. 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